Phrase recognition method and apparatus

ABSTRACT

A phrase recognition method breaks streams of text into text &#34;chunks&#34; and selects certain chunks as &#34;phrases&#34; useful for automated full text searching. The phrase recognition method uses a carefully assembled list of partition elements to partition the text into the chunks, and selects phrases from the chunks according to a small number of frequency based definitions. The method can also incorporate additional processes such as categorization of proper names to enhance phrase recognition. The method selects phrases quickly and efficiently, referring simply to the phrases themselves and the frequency with which they are encountered, rather than relying on complex, time-consuming, resource-consuming grammatical analysis, or on collocation schemes of limited applicability, or on heuristical text analysis of limited reliability or utility.

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BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to automated indexing of full-textdocuments to identify the content-bearing terms for later documentretrieval. More specifically, the invention relates tocomputer-automated identification of phrases which are useful inrepresenting the conceptual content of documents for indexing andretrieval.

2. Related Art

A type of content-bearing term is the "phrase", a language device usedin information retrieval to improve retrieval precision. For example,the phrase "product liability" indicates a concept that neither of thetwo component words can fully express. Without this phrase, a retrievalprocess is unable to find the documents in which the concept isdiscussed.

In traditional Boolean retrieval systems, phrase recognition is not anissue. The systems are known as post-coordination indexing systems inthat phrases can be discovered through examining the adjacencyrelationships among search words during the process of merging invertedlists associated with the words.

However, in modern information retrieval systems, the statisticaldistribution characteristics of index terms are crucial to the relevanceranking process, and it is desirable to recognize phrases and derivetheir statistical characteristics in advance. In addition, infabricating hypertext databases, recognized phrases are necessary forhypertext linkage.

Known phrase recognition methods include three types: machinetranslation, statistical text analysis and heuristical text analysis.

First, machine translation's approach to recognizing phrases (known ascompound structures) is to analyze part-of-speech tags associated withthe words in an input text string, usually a sentence. Noun phrases andverb phrases are two examples of such phrases. Syntactical context andlexical relationships among the words are key factors that determinesuccessful parsing of the text. In machine translation, the goal is notof finding correct phrases, but of discovering the correct syntacticalstructure of the input text string to support other translation tasks.It is infeasible to use this syntactical parsing method for processingcommercial full-text databases; the method is inefficient and is inpractical terms not scalable. Regarding machine translation, referencemay be made to U.S. Pat. Nos. 5,299,124, 5,289,375, 4,994,966,4,914,590, 4,931,936, and 4,864,502.

The second method of analysis, statistical text analysis, has two goals:disambiguating part of speech tagging, and discovering noun phrases orother compound terms. The statistics used include collocationinformation or mutual information, i.e., the probability that a givenpair of part-of-speech tags or a given pair of words tends to appeartogether in a given data collection. When a word has more than onepart-of-speech tag associated with it in a dictionary, consulting thepart of speech of the next word and calculating the probability ofoccurrence of the two tags would help select a tag. Similarly, a pair ofwords that often appear together in the collection is probably a phrase.However, statistical text analysis requires knowledge of collocationthat can only be derived from an known data collection.Disadvantageously, the method is not suitable for processing unknowndata. Regarding statistical text analysis, reference may be made to U.S.Pat. Nos. 5,225,981, 5,146,405, and 4,868,750.

The third method of analysis, heuristical text analysis, emphasizestextual pattern recognition. Textual patterns include any recognizabletext strings that represent concepts, such as company names, peoples'names, or product names. For example, a list of capital words followedby a company indicator like "Limited" or "Corp" is an example patternfor recognizing company names in text. The heuristical text analysismethod requires strong observation ability from a human analyst. Due tothe limitation of humans' observation span, heuristical text analysis isonly feasible for small subject domains (e.g., company name, productnames, case document names, addresses, etc.). Regarding heuristical textanalysis, reference may be made to U.S. Pat. Nos. 5,410,475, 5,287,278,5,251,316, and 5,161,105.

Thus, machine translation methods, involving potentially complexgrammatical analysis, are too expensive and too error-prone for phraserecognition. Statistical text analysis, being based on collocation andbeing purely based on statistics, is still expensive because of therequired full scale of part-of-speech tagging and pre-calculatingcollocation information, and also has difficulties processing unknowndata without the collocation knowledge. Finally, heuristical textanalysis, relying on "signal terms", is highly domain-dependent and hastrouble processing general texts.

Thus, there is a need in the art for a simple, time-efficient,resource-efficient, and reliable phrase recognition method for use inassisting text indexing or for forming a statistical thesaurus. It isdesired that the phrase recognition method be applicable to bothindividual documents and to large collections of documents, so that theperformance of not only real-time on-line systems, but also distributedand mainframe text search systems can be improved. It is also desiredthat the phrase recognition method have engineering scalability, and notbe limited to particular domains of knowledge.

The present invention is directed to fulfilling these needs.

SUMMARY OF THE INVENTION

The present invention provides a phrase recognition method which breakstext into text "chunks" and selects certain chunks as "phrases" usefulfor automated full text searching. The invention uses a carefullyassembled list of partition words to partition the text into the chunks,and selects phrases from the chunks according to a small number offrequency-based definitions. The invention can also incorporateadditional processes such as categorization of proper names to enhancephrase recognition.

The invention achieves its goals quickly and efficiently, referringsimply to the phrases and the frequency with which they are encountered,rather than relying on complex, time-consuming, resource-consuminggrammatical analysis, or on collocation schemes of limitedapplicability, or on heuristical text analysis of limited reliability orutility.

Additional objects, features and advantages of the invention will becomeapparent when the following Detailed Description of the PreferredEmbodiments is read in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is better understood by reading the following DetailedDescription of the Preferred Embodiments with reference to theaccompanying drawing figures, in which like reference numerals refer tolike elements throughout, and in which:

FIG. 1 illustrates an exemplary hardware configuration on which theinventive phrase recognition method may be executed.

FIG. 2 illustrates another exemplary hardware environment in which theinventive phrase recognition method may be practiced.

FIG. 3 is a high level flow chart schematically indicating execution inan embodiment of the phrase recognition method according to the presentinvention.

FIG. 4A is a flow chart schematically indicating execution in a modulefor partitioning text and generating text chunks.

FIG. 4B is a flow chart indicating execution of a module for selectingphrases using the data memory structure diagram of FIG. 5.

FIG. 5 is a data memory structure diagram schematically illustratingdata flow during the inventive phrase recognition method (FIGS. 3, 4A,4B) and corresponding memory allocation for various types of data usedin accordance with the process.

FIG. 6A is a flow chart of an optional processing module forconsolidating with a thesaurus.

FIG. 6B is a flow chart of an optional processing module for processingphrases with prepositions.

FIG. 6C is a flow chart of an optional processing module for trimmingphrases with their collection frequencies.

FIG. 6D is a flow chart of an optional processing module forcategorizing proper names.

FIGS. 7A, 7B and 7C illustrate exemplary applications of the inventivephrase recognition method according to the present invention. Inparticular: FIG. 7A indicates a user's viewing of a document inaccordance with a suitable on-line text search system, and invoking theinventive phrase recognition method to search for additional documentsof similar conceptual content; FIG. 7B schematically illustratesimplementation of the phrase recognition method in a batch phraserecognition system in a distributed development system; FIG. 7Cschematically illustrates application of the inventive phraserecognition method in a batch process in a mainframe system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In describing preferred embodiments of the present invention illustratedin the drawings, specific terminology is employed for the sake ofclarity. However, the invention is not intended to be limited to thespecific terminology so selected, and it is to be understood that eachspecific element includes all technical equivalents which operate in asimilar manner to accomplish a similar purpose.

The concept of the present invention is first described on a particularexample of a text stream. Then, block diagrams and flow charts aredescribed, which illustrate non-limiting embodiments of the invention'sstructure and function.

Very briefly, a preferred embodiment of the inventive method partitionsan input text stream based on punctuation and vocabulary. As the methodprocesses the text stream sequentially, it inserts partition symbolsbetween words if certain punctuation exists, such as a comma, end ofsentence, or change in capitalization. Further, each word encountered ischecked against one or more vocabulary lists, and may be discarded andreplaced by a partition symbols, based on the word and where it isencountered.

After the document is thus processed, a set of candidate terms and"phrases" (a series of non-partitioned words) is produced. In thepreferred embodiment, solitary words (individual words immediatelysurrounded by partitions) are ignored at this point. The phrases areprocessed to determine which phrases occur with higher frequency.Preferably, shorter phrases which occur with higher frequency and assubsets of lower-frequency but lengthier phrases are also sought. A setof phrases meeting or exceeding a given threshold frequency is produced.

The inventive method is more easily understood with reference to aparticular example.

As mentioned above, members of a list of words (including punctuation)serve as "break points" to form text "chunks" within input text. A first(rudimentary) list includes words can be used as "stop words". The stopwords usually carry little semantic information because they existmerely for various language functions. This list has a few hundredmembers and includes articles (e.g., "a", "the"), conjunctions (e.g.,"and", "or"), adverbs (e.g., "where", "why"), prepositions (e.g., "of","to", "for"), pronouns (e.g., "we", "his"), and perhaps some numericitems.

However, this first list is too short for the present phrase recognitionmethod because it causes generation of a list of text chunks that aretoo long to allow efficient generation of desirable phrases. Additionalstop words or other partition items are needed for reducing the size ofthe text chunks, so that more desirable phrases may be found.

The following example of text illustrates this problem. In this example,the text "chunks" are within square brackets, with the text chunks beingseparated by an members of the list of stop words (break points):

Citing! what is called newly conciliatory comments! by the leader! ofthe Irish Republican Army!'s political wing!, the Clinton Administrationannounced today! that it would issue! him a visa! to attend! aconference! on Northern Ireland! in Manhattan! on Tuesday!. TheAdministration! had been leaning! against issuing! the visa! to theofficial!, Gerry Adams!, the head! of Sinn Fein!, leaving! the WhiteHouse caught! between the British Government! and a powerful bloc! ofIrish-American legislators! who favored! the visa!. (Parsed text basedon rudimentary list)

Since desirable phrases include noun phrases (e.g., "ice cream"),adjective-noun phrases (e.g., "high school"), participle-noun phrases(e.g., "operating system"), and proper names (e.g., "White House"), itis safe to add adverbs (e.g., "fully") and non-participle verbs (e.g.,"have", "is", "obtain") to the list of stop words to form an enhancedstop word list. This enhanced stop word list allows the method toprovide smaller text chunks, yet is still compact enough for efficientlook-up by computer. With the enhanced list, the above example text isparsed into chunks and stop words as follows:

Citing! what is called! newly conciliatory comments! by the leader! ofthe Irish Republican Army!'s political wing!, the ClintonAdministration! announced today! that it would issue him a visa! toattend a conference! on Northern Ireland! in Manhattan! on Tuesday!. TheAdministration! had been leaning! against issuing! the visa! to theofficial!, Gerry Adams!, the head! of Sinn Fein!, leaving! the WhiteHouse! caught between the British Government! and a powerful bloc! ofIrish-American legislators! who favored the visa!. (Second parsed textbased on enhanced list)

The theoretical justification of using this enhanced list derives fromtwo sources.

A first justification is that this list only represents about 13% ofunique words in a general English dictionary. For example, in the Mobydictionary of 214,100 entries, there are 28,408 words that can be putinto the list. This fact ensures that semantic, information in texts ismaintained at a maximum level.

A second justification involves the lexical characteristics of thesewords. Most of the words bear little content. This second fact reducesthe risk of losing semantic information in the text.

The basic concept of the invention having been described, particularimplementations of its structure and function are now presented.

As will readily be appreciated, the invention is preferably embodied assoftware, instruction codes capable of being executed by digitalcomputers, including commercially available general purpose digitalcomputers well known to those skilled in the art. The particularhardware on which the invention may be implemented varies with theparticular desired application of the inventive phrase recognitionmethod. Three examples of the such application of the phrase recognitionmethod are described in greater detail below, with reference to FIGS.7A, 7B, and 7C. Briefly, the dynamic recognition method involved in anon-line system (FIG. 7A) may be implemented in IBM 370 assembly languagecode. Alternatively, in a batch recognition system in a distributeddevelopment system (FIG. 7B), the phrase recognition method may beimplemented on a SUN work station using the PERL script interpretiveprototyping language. As a still further implementation, the inventivephrase recognition method may be implemented on an Amdahl AMD5995-1400-a mainframe so that another batch phrase recognition system(FIG. 7C) may be realized. Of course, the scope of the invention shouldnot be limited by these exemplary embodiments or applications.

Embodiments of the inventive phrase recognition method may beimplemented as a software program including a series of executablemodules on a computer system. As shown in FIG. 1, an exemplary hardwareplatform includes a central processing unit 110. The central processingunit interacts with a human user through a user interface 112. The userinterface is used for inputting information into the system and forinteraction between the system and the human user. The user interface112 includes, for example, a video display 113 and a keyboard 115. Acomputer memory 114 provides storage for data and software programswhich are executed by the central processing unit 110. Auxiliary memory116, such as a hard disk drive or a tape drive, provides additionalstorage capacity and a means for retrieving large batches ofinformation.

All components shown in FIG. 1 are of a type well known in the art. Forexample, the FIG. 1 system may include a SUN® work station including theexecution platform Sparc 2 and SUN OS Version 4.1.2., available from SUNMICROSYSTEMS of Sunnyvale, Calif. Of course, the system of the presentinvention may be implemented on any number of modern computer systems.

A second, more complex environment in which the inventive phraserecognition method may be practiced is shown in FIG. 2. In particular, adocument search and retrieval system 30 is shown. The system allows auser to search a subset of a plurality of documents for particular keywords or phrases. The system then allows the user to view documents thatmatch the search request. The system 30 comprises a plurality of Searchand Retrieval (SR) computers 32-35 connected via a high speedinterconnection 38 to a plurality of Session Administrator (SA)computers 42-44.

Each of the SR's 32-35 is connected to one or more document collections46-49, each containing text for a plurality of documents, indexestherefor, and other ancillary data. More than one SR can access a singledocument collection. Also, a single SR can be provided access to morethan one document collection. The SR's 32-35 can be implemented using avariety of commercially available computers well known in the art, suchas Model EX100 manufactured by Hitachi Data Systems of Santa Clara,Calif.

Each of the SA's 42-44 is provided access to data representing phraseand thesaurus dictionaries 52-54. The SA's 42-44 can also be implementedusing a variety of commercially available computers, such as Models 5990and 5995 manufactured by Amdahl Corporation of Sunnyvale Calif. Theinterconnection 38 between the SR's and the SA's can be any one of anumber of two-way high-speed computer data interconnections well knownin the art, such as the Model 7200-DX manufactured by Network SystemsCorporation of Minneapolis, Minn.

Each of the SA's 42-44 is connected to one of a plurality of front endprocessors 56-58. The front end processors 56-58 provide a connection ofthe system 30 one or more commonly available networks 62 for accessingdigital data, such as an X.25 network, long distance telephone lines,and/or SprintNet. Connected to the network 62 are plural terminals 64-66which provide users access to the system 30. Terminals 64-66 can be dumbterminals which simply process and display data inputs and outputs, orthey can be one of a variety of readily available stand-alone computers,such as IBM or IBM-compatible personal computers. The front endprocessors 56-58 can be implemented by a variety of commerciallyavailable devices, such as Models 4745 and 4705 manufactured by theAmdahl Corporation of Sunnyvale Calif.

The number of components shown in FIG. 2 are for illustrative purposesonly. The system 30 described herein can have any number of SA's, SR's,front end processors, etc. Also, the distribution of processingdescribed herein may be modified and may in fact be performed on asingle computer without departing from the spirit and scope of theinvention.

A user wishing to access the system 30 via one of the terminals 64-66will use the network 62 to establish a connection, by means well knownin the art, to one of the front end processors 56-58. The front endprocessors 56-58 handle communication with the user terminals 64-66 byproviding output data for display by the terminals 64-66 and byprocessing terminal keyboard inputs entered by the user. The data outputby the front end processors 56-58 includes text and screen commands. Thefront end processors 56-58 support screen control commands, such as thecommonly known VT100 commands, which provide screen functionality to theterminals 64-66 such as clearing the screen and moving the cursorinsertion point. The front end processors 56-58 can handle other knowntypes of terminals and/or stand-alone computers by providing appropriatecommands.

Each of the front end processors 56-58 communicates bidirectionally, bymeans well known in the art, with its corresponding one of the SA's42-44. It is also possible to configure the system, in a manner wellknown in the art, such that one or more of the front end processors cancommunicate with more than one of the SA's 42-44. The front endprocessors 56-58 can be configured to "load balance" the SA's 42-44 inresponse to data flow patterns. The concept of load balancing is wellknown in the art.

Each of the SA's 42-44 contains an application program that processessearch requests input by a user at one of the terminals 64-66 and passesthe search request information onto one or more of the SR's 32-35 whichperform the search and returns the results, including the text of thedocuments, to the SA's 42-44. The SA's 42-44 provide the user with textdocuments corresponding to the search results via the terminals 64-66.For a particular user session (i.e. a single user accessing the systemvia one of the terminals 64-66), a single one of the SA's 42-44 willinteract with a user through an appropriate one of the front endprocessors 56-58.

Preferably, the inventive phrase recognition method is implemented inthe session administrator SA computers 42-44, with primary memory beingin the SA computer itself and further memory being illustrated withinelements 52-54.

The principles on which the inventive method is based, and hardwaresystems and software platforms on which it may be executed, having beendescribed, a preferred embodiment of the inventive phrase recognitionmethod is described as follows.

FIG. 3 is a high level flow diagram of the phrase recognition method ofthe preferred embodiment.

Referring to FIG. 3, the invention uses a carefully assembled list ofEnglish words (and other considerations such as punctuation) in aPartition Word List (more generally referred to as a Partition EntityList, or simply Partition List) to partition one or more input textstreams into many text chunks. This partitioning process is illustratedin block 10.

Block 20 indicates selection of phrases from among the chunks of text,according to frequency based definitions. A Phrase List, including theselected phrases, results from execution of the process in block 20.During the phrase selection process, solitary words (single-wordchunks), as well as words from the decomposed phrases, can be maintainedseparate from the Phrase List as optional outputs for other indexingactivities.

Details of processes 10 and 20 are described with reference to FIGS. 4Aand 4B.

The invention can optionally incorporate one or more other processes,generically indicated as element 30. Such optional process may includecategorization (examples described with reference to FIGS. 6A-6D) toenhance the list of recognized phrases.

FIG. 4A is a flow chart of FIG. 3 module 10, for partitioning text andgenerating text chunks.

FIG. 4A shows how the method, given a piece of text, partitions the textinto many small text chunks. A critical component in this method is thePartition List (including words and punctuation) whose members serve asbreak points to generate the text chunks.

As mentioned above, a Partition List ideally allows parsing of text intoshort phrases, but is itself still compact enough for efficient computerlook-up during the parsing process. Preferably, the Partition List isgenerated using not only articles, conjunctions, adverbs, prepositions,pronouns, and numeric items, but also adverbs and verbs, to form anenhanced list.

The text partitioning process starts off with looking up encounteredtext in the Partition List (at block 101) and replacing every matchedpartition word or other partition entity with a partition tag such as"####" (shown at block 102).

Additional partition tags are added into those text chunks at the pointwhere there is a case change, either from lower case to upper case orvice versa (shown at block 103). Block 104 indicates generation of thetext chunk list which preserves the natural sequence of the chunks asencountered in the text.

The frequency information for each chunk in the list is collected byscanning the text chunks in their natural sequence. The first occurrenceof each unique chunk in the sequence is registered as a new entry withits frequency as 1. Subsequent occurrences are registered byincrementing its frequency count by 1. This generation of occurrencefrequencies in association with the respective chunks is indicated byblock 105.

FIG. 4B is a flow chart of FIG. 3 module 20, illustrating details of apreferred process for selecting which chunks are phrases.

FIG. 5 is a data memory structure diagram showing how data may bearranged in memory for the process, and how data flows into and out ofvarious steps of the process. More specifically, the steps from FIG. 3of text partitioning 10, phrase selection 20, and optional processing 30(reproduced on the left side of FIG. 5) are illustrated in conjunctionwith an exemplary data memory structure diagram (on the right side ofFIG. 5) to schematically illustrate data flow between major functionalprocedures and data structures. The various lists shown in the exemplarymemory blocks on the right side of FIG. 5 are understood to include listmembers in conjunction with their respective frequencies of occurrence.

The memory (broadly, any data storage medium such as RAM and/or magneticdisk and/or optical disk and/or other suitable computer readable medium)may be structured in memory blocks as schematically illustrated. A textstream file 300 and a Partition List 310 are used as inputs to thepartitioning process 10 of the inventive phrase recognition method. Thepartitioning process 10 provides a chunk list (understood as includingcorresponding chunk frequencies) 315. Chunk list 315 is used by thephrase selection process 20 of the inventive phrase recognition method.

The partitioning process produces various groupings of chunks, each withtheir respective frequencies of occurrence within the text stream. Thesegroupings of chunks are illustrated on the right side of FIG. 5, withthe understanding that the invention should not be limited to theparticular memory structure so illustrated.

Specifically, lower case words (that is, single-word chunks) are inmemory block 320, capitalized or "allcaps" single-word chunks are inmemory block 325, a Proper Name List (preferably of greater than oneword, each being capitalized or in allcaps) is in memory block 330,lower case phrases of greater than one word occurring more than once arein memory block 335, lower case phrases of greater than one word whichwere encountered only once are in memory block 345, and, optionally,acronyms are in memory block 350.

A synonym thesaurus in memory block 375 may be used in an optionalprocess 30. A phrase frequency list derived from a collection of pluraldocuments in which the phrase frequency throughout the collection isgreater than a threshold, in memory block 380, may also be used in anoptional processing procedure 30. Further, one or more special indicatorlists, generally indicated as 385A-385E (company indicators, geographicnames, product names, organization indicators, English first names,respectively, some of which are exemplified in the attached List) maycontribute to certain optional categorizing processes, and result incorresponding name lists (company names, geographic location names,product names, organization names, and English names) generallyindicated as 390A-390E.

Referring again to FIG. 4B, after the text chunk list is produced, it isthe time to make decision whether each chunk in the list is a phraseuseful for representing conceptual content of documents. The inventivephrase recognition method uses the frequency information of two types ofthe partitioned text chunks (namely, the proper names in block 330 andthe lower case phrases in blocks 335 and 345) to make final phraseselection decisions. Preferably, the invention focuses on lower casephrases of more than one word, or on proper names ("John Hancock","United States").

Referring to FIGS. 4B and 5, at block 201, entries consisting of asolitary lower case word are not selected as phrases. Rejected entriesare stored in memory block 320.

As shown at block 202, those chunks that include plural lower case wordsare determined to be phrases only if they occur at least twice in thetext stream. These chunks are stored in memory block 335. Chunks notfitting these criteria are stored in block 345 for further processing.

For chunks consisting of a solitary upper case word (either the firstletter being capitalized or "allcaps"), no phrase decision is made atthis stage, as shown at block 203. Such chunks are stored in memoryblock 325.

In block 204, chunks including plural upper case words are determined tobe proper names and are stored in a Proper Name List in memory block330.

Finally, other text chunks not fitting the previous criteria are simplydiscarded at this time, as indicated at block 205.

Next, block 206 examines the lower case phrases having a singleoccurrence from memory block 245. They are examined for having one ofits sub-phrases as part of an existing lower case phrase in the list.For efficiency, a sub-phrase may be defined to be the first or last twoor three words in the phrases. When the existence of a sub-phrase isdetected, it is merged into the corresponding phrase in the list inmemory block 335, and its frequency count is updated. Otherwise, and thelower case phrase is decomposed into individual words for updating thelower case word list in memory block 320 as an optional output.

As a result of this sub-phrase mapping in block 206, in our example thelist is reduced to a list of lower case phrases and a list of propernames, both with their respective frequency counts:

political wing, 2!

Citing, 1!

Irish Republican Army, 1!

Clinton Administration, 1!

Northern Ireland, 1!

Manhattan, 1!

Tuesday, 1!

Administration, 1!

Gerry Adams, 1!

Sinn Fein, 1!

White House, 1!

British Government, 1!

The singleton upper case word could be used for referencing an existingproper name in the proper name list. To make the final frequency countaccurate, the method makes one additional scan to the Proper Name List330. It consolidates the upper case word that is either the first or thelast word of a previously recognized proper name, and updates itsfrequency count. This use of upper case single words in memory block 325to revise the Proper Name List 330 is indicated at block 207. The methodstores the other upper case words in the upper case word list 325 as anoptional output.

A special case of the singleton upper case word is that of the acronym.An acronym is defined either as a string of the first character of eachword (which is neither a preposition nor a conjunction) in a proper nameor as a string of the first character of each word in a proper namefollowed by a period. As indicated at block 208, when an acronym inmemory block 325 maps to a proper name in the proper name list 330, thefrequency count of the proper name is incremented, and the pair of theproper name and its acronym is copied into an acronym list 350 as anoptional output.

In our example, this reference checking process further reduces theproper name list in this example to the following:

Irish Republican Army, 1!

Clinton Administration, 2!

Northern Ireland, 1!

Gerry Adams, 1!

Sinn Fein, 1!

White House, 1!

British Government, 1!

If no additional processing is necessary, this method concludes bycombining the lower case phrase list in memory block 335 and the ProperName List in memory block 330 into a single Phrase List 340 which isprovided as the final output of the phrase selection process 20.

In another embodiment, the lower case phrases with frequency=1 in memoryblock 345 are also included in the consolidation, in addition to theProper Name List in memory block 330 and the lower case phrases havingfrequency greater than 1 in memory block 335. The choice of eitherincluding or excluding the lower case phrases in memory block 345 isdetermined by a frequency threshold parameter which determines thenumber of times a lower case phrase must be encountered before it isallowed to be consolidated into the final Phrase List.

The example shown in FIG. 5 has this threshold set to 2, so that thosephrases encountered only once (in memory block 345) are excluded fromthe consolidated Phrase List 340. The dotted line extending downwardfrom Phrase List 340 to include memory block 345 shows how lower casephrases encountered only once can be included in the Phrase List ifdesired, however.

In any event, the consolidation of memory blocks into a single PhraseList is indicated at block 209.

For this text stream example, the final Phrase List is as follows:

political wing, 2!

Irish Republican Army, 1!

Clinton Administration, 2!

Northern Ireland, 1!

Gerry Adams, 1!

Sinn Fein, 1!

White House, 1!

British Government, 1!

The invention envisions that optional processes are available forfurther enhancing the recognized phrases.

FIG. 6A is a flow chart of an optional processing module forconsolidating with a synonym thesaurus.

Referring to FIG. 6A, the Phrase List can be further reduced with asynonym thesaurus, as indicated at block 301. The synonym thesaurus maybe any suitable synonym thesaurus available from commercial vendors. Asan example, the Phrase List may map "White House" to "ClintonAdministration." Using a synonym thesaurus is risky because its contentsmay not reflect the intended conceptual content of the text, andtherefore may cause mapping errors. For example, it would be problematicif a synonym thesaurus maps "Bill Clinton" to "White House", because thetwo terms are not always equivalent.

FIG. 6B is a flow chart of an optional processing module for processingphrases with prepositions.

Referring to FIG. 6B, when a desirable lower case phrase contains one ofa small set of prepositions (e.g., "right to counsel", "standard ofproof"), the method takes the set out of the Partition List used forgenerating text chunks so that the phrase including the preposition hasan opportunity to reveal itself as being part of a "good" phrase. Thisprocess is indicated as block 302.

Since it is statistically unlikely that any given occurrence of apreposition is in a "good" phrase, this optional process consumessubstantial time for a relatively small increase in phrases, and isconsidered optional.

It is necessary to have another process to further examine theunqualified phrase in memory block 345 that contains one of the selectedprepositions, whether the sub-phrase on the left of the preposition orthe sub-phrase on the right constitutes a valid phrase in the lower casephrase list in memory block 335. This process is illustrated as block303.

As a result of process blocks 302, 303, memory block 335 may be updated.

FIG. 6C is a flow chart of an optional processing module for trimmingphrases with their collection frequencies.

Referring to FIG. 6C, still another optional process is that of editingthe list of the Proper Name List 330 and lower case phrases 335 withadditional frequency information 380 gathered from a text collection ofmore than one document. The assumption here is that, the more authorswhich use a phrase, the more reliable the phrase is for uniquelyexpressing a concept. In other words, a phrase occurring in more thanone document is a "stronger" phrase than another phrase occurring onlyin a single document.

Here, the "collection frequency" of a phrase is the number of documentsthat contain the phrase. A collection frequency threshold (e.g., fivedocuments) can be set to trim down those phrases whose collectionfrequencies are below the threshold, as indicated at block 304.Essentially, FIG. 6C trims the entire Phrase List 340, including entriesfrom either memory block 330 or 335.

When collection frequency information is available (as illustrated bymemory block 380), the minimum frequency requirement of two encountersfor the lower case phrases within a text (see FIG. 5) can be lowered toone encounter. "Mistaken" phrases will be rejected when consulting thecollection frequency information when considering multiple documents.

FIG. 6D is a flow chart of an optional processing module forcategorizing proper names.

Referring now to FIG. 6D, after proper names are identified and arestored in the Proper Name List 330, it is possible to categorize theminto new sets of pre-defined groups, such as company names, geographicnames, organization names, peoples' names, and product names.

A list 385A of company indicators (e.g., "Co." and "Limited") is usedfor determining whether the last word in a proper name is such anindicator, and thereafter for categorizing it into the group of companyname. Any word after this indicator is removed from the proper name.

With the knowledge of the company name, it may be useful to check theexistence of the same company name in the list that does not have theindicator word. If the search is successful, the frequency count of thecompany name is updated. The recognized company names are kept in aCompany Names list 390A as an optional output, as indicated at block305.

Similarly, a list 385B of geographic names or a list 385C of productnames may be used for looking up whether a proper name has a match andthereafter for categorizing it into the list of geographic names or alist of product names, respectively. The recognized geographic names orproduct names are kept in Geographic Location Names 390B or ProductNames 390C lists as optional outputs, as indicated at blocks 306 and307.

A list 385D of words that designate organizations is used fordetermining whether the first or the last word of a proper name is theindicator of organization, and thereafter for categorizing it into thegroup of organizations. The recognized organization names may be kept inan Organization Names List 390D as an optional output, as indicated atblock 308.

Finally a list 385E of English first names is used for determiningwhether the first word of a proper name is a popular first name andthereafter for categorizing it into the group of peoples' names. Anyword before the first name is removed from the proper name. The morecomprehensive the lists are, the more people names can be categorizedproperly. The recognized people names are kept in a separate EnglishNames list 390E as an optional output for other indexing activities, asindicated at block 309.

Appendices A through E present an exemplary Partition List 310 andexemplary Special Indicator/Name lists 385A-385E.

The inventive method having been described above, the invention alsoencompasses apparatus (especially programmable computers) for carryingout phrase recognition. Further, the invention encompasses articles ofmanufacture, specifically, computer readable memory on which thecomputer-readable code embodying the method may be stored, so that, whenthe code used in conjunction with a computer, the computer can carry outphrase recognition.

Non-limiting, illustrative examples of apparatus which inventionenvisions are described above and illustrated in FIGS. 1 and 2. Eachconstitutes a computer or other programmable apparatus whose actions aredirected by a computer program or other software.

Non-limiting, illustrative articles of manufacture (storage media withexecutable code) may include the disk memory 116 (FIG. 1), the diskmemories 52-54 (FIG. 2), other magnetic disks, optical disks,conventional 3.5-inch, 1.44 MB "floppy" diskettes or other magneticdiskettes, magnetic tapes, and the like. Each constitutes a computerreadable memory that can be used to direct the computer to function in aparticular manner when used by the computer.

Those skilled in the art, given the preceding description of theinventive method, are readily capable of using knowledge of hardware, ofoperating systems and software platforms, of programming languages, andof storage media, to make and use apparatus for phrase recognition, aswell as computer readable memory articles of manufacture which, whenused in conjunction with a computer can carry out phrase recognition.Thus, the invention's scope includes not only the method itself, butapparatus and articles of manufacture.

Applications of the phrase recognition method. The phrase recognitionmethod described above can be used in a variety of text searchingsystems. These include, but need not be limited to, dynamic phraserecognition in on-line systems, batch phrase recognition in adistributed development system, and batch phrase recognition in amainframe system. The following description of the applications of theinventive phrase recognition method is illustrative, and should notlimit the scope of the invention as defined by the claims.

In an on-line system (OLS) envisioned as a target application for theinventive phrase recognition method, a user viewing a current documentand entering a command to search for documents of similar conceptualcontent must wait for the phrase recognition process to be completed.Accordingly, the efficiency of the inventive phrase recognition methodis important, as it allows reduced response time and uses minimalresources in a time-sharing environment.

According to the application of the invention in a given on-line system,the method processes the text in a single document in real time toarrive at a list of "good" phrases, namely, ones which can be used asaccurate and meaningful indications of the document's conceptualcontent, and which can be used as similarly accurate and meaningfulqueries in subsequent search requests. In particular, according to apreferred application, the Phrase List derived from the single documentis used to construct a new search description to retrieve additionaldocuments with similar conceptual content to the first document.

This implementation of the phrase recognition method may, for example,be embedded in session administrator (FIG. 2) or other software whichgoverns operation of the computer system on which the phrase recognitionmethod. Of course, the particular implementation will vary with thesoftware and hardware environment of the particular application inquestion.

FIG. 7A indicates a user's viewing of a document in accordance with asuitable on-line text search system, and invoking the inventive phraserecognition method to search for additional documents of similarconceptual content. In particular, block 701 assumes a user viewing agiven document enters a command (such as ".more") to retrieve moredocuments similar in conceptual content to the current one being viewed.

When the ".more" command is entered, control passes to block 702 whichindicates retrieval of the document being viewed and passing it to thesession administrator or other software which includes the inventivephrase recognition software.

Block 703 indicates execution of the inventive phrase recognition methodon the text in the retrieved document. A candidate phrase list isgenerated, based on that single document.

Block 704 indicates how the candidate phrase list generated from thesingle document may be validated against an existing (larger) phrasedictionary. The static phrase dictionary may be generated as describedbelow, with reference to the batch phrase recognition application in adistributed development system.

If a candidate phrase does not already exist in the phrase dictionary,the candidate phrase is broken down into its component words.Ultimately, a list of surviving phrases is chosen, based on frequency ofoccurrence.

At decision block 705, if at least a given threshold number of words orphrases (e.g., five words or phrases) is extracted, control passes fromdecision block 705 to block 708, described below.

If, however, the given threshold number of words or phrases are notextracted, control passes from decision block 705 along path 706 back toblock 701, after displaying an error message at block 707 whichindicates that the displayed current document could not successfully beprocessed under the ".more" command.

Block 708 indicates that the newly-added words or phrases are added tothe search query which previously resulted in the user's viewing thecurrent document. Block 709 indicates the system's displaying the new"combined" search query to the user. The user may edit the new query, ormay simply accept the new query by pressing "enter".

FIG. 7B schematically indicates implementation of the phrase recognitionmethod in a batch phrase recognition system in a distributed developmentsystem.

In contrast to the implementation of the on-line system of FIG. 7A, inthe application shown in FIG. 7B, the phrase recognition method isapplied to a large collection of documents, and produces a list ofphrases associated with the entire collection. As mentioned above withreference to FIG. 7A, the phrase dictionary may be generated by thisbatch recognition process in the "distributed development domain" (DDD)when there is an abundance of idle system resources. When the on-linesystem then uses the resultant phrase dictionary, the phrase dictionaryis essentially static, having been generated and modified outside theon-line sessions.

The FIG. 7B application takes longer to execute than the single-documentphrase recognition process occurring in the dynamic phrase recognitionin the on-line application. Accordingly, the FIG. 7B process ispreferably executed as a batch process at times when overall systemusage is not impaired, such as overnight. In particular, the softwareimplementation of the phrase recognition/phrase dictionary buildingprocess may be implemented on SUN work stations.

As a background to FIG. 7B, a developer's control file defines whichdocuments, and/or which portions of the documents, should be processedin a given run. Block 723 indicates a filtering process which filtersout documents and portions of documents which are not desired tocontribute to the phrase dictionary, based on the control file. Block724 indicates application of the inventive phrase recognition method tothe documents and portions of documents which have passed through filterprocess 723.

The output of the phrase recognition process is a phrase list (PL)which, in the illustrated non-limiting embodiment, is stored as astandard UNIX text file on disk. In a preferred embodiment, single-wordterms which are encountered are discarded, so that only multiple wordphrases are included in the phrase list (PL).

For simplicity, each phrase is provided on a single line in the file.Block 725 indicates how the UNIX file is sorted using, for example, thestandard UNIX sort utility, causing duplicate phrases to be groupedtogether. Block 725 also calculates the frequency of each of the groupedphrases.

If a given phrase occurs less than a given threshold number of times(e.g., five times as tested by decision block 727) it is discarded, asindicated by decision block 726. Only phrases which have beenencountered at least that threshold number of times survive to beincluded in the revised Phrase List, as shown in block 728.

The revised Phrase List is then transferred from the SUN work station toits desired destination for use in, for example, the on-line systemdescribed above. It may also be transferred to a main frame computerusing a file transfer protocol FTP, to be processed by a phrasedictionary building program and compiled into a production phrasedictionary. This process is shown illustrated as block 729.

Referring now to FIG. 7C, the application of the inventive phraserecognition method on a mainframe system is schematically illustrated.In the illustrated application, the phrase recognition method isimplemented as a batch process in a production mainframe. The processinvolves a random sample of documents from a larger collection ofdocuments, and produces a set of phrases for each document processed.The process is preferably executed when system resources are nototherwise in high demand, such as overnight. The process of FIG. 7C isespecially useful for use with statistical thesauri.

As a background, it is assumed that phrases may be considered to be"related" to each other if they occur in the same document. This"relationship" can be exploited for such purposes as expanding a user'ssearch query. However, in order to provide this ability, large number ofdocuments must first be processed.

Referring again to FIG. 7C, block 733 indicates the filtering ofdocuments and portions thereof in accordance with specifications from acontrol file, in much the same manner as described with reference toFIG. 7B. Block 734 indicates the application of the inventive phraserecognition method to the documents which pass the filter. One set ofterms (single words, phrases or both) is produced for each document andstored in respective suitably formatted data structure on a disk orother storage medium.

Further details of implementation of the applications of the inventivephrase recognition method depend on the particular hardware system,software platform, programming languages, and storage media beingchosen, and lie within the ability of those skilled in the art.

The following List are exemplary, illustrative, non-limiting examples ofa Partition List and other lists which may be used with an embodiment ofthe phrase recognition method according to the present invention.

    ______________________________________                                        List A                                                                        Example of PARTITION LIST                                                     (On-Line System with News Data)                                               Copyright 1995                                                                LEXIS-NEXIS, a Division of Reed Elsevier Inc.                                 ______________________________________                                        A          BAITH      EVERY-     HEREOF                                                             THING                                                   A.M        BE         EXCEPT     HEREON                                       ABOUT      BECAME     FEB        HERETO                                       ABOVE      BECAUSE    FEBRUARY   HEREWITH                                     ACROSS     BEEN       FEW        HERN                                         AFFECT     BEFORE     FEWER      HERSELF                                      AFTER      BEING      FIFTEEN    HIC                                          AGAIN      BELOW      FIVE       HIM                                          AGO        BETWEEN    FOR        HIMSELF                                      ALL        BOTH       FOUR       HIS                                          ALREADY    BUT        FOURTEEN   HIS'N                                        ALSO       BY         FRI        HISSELF                                      ALTHOUGH   CAN        FRIDAY     HOC                                          ALWAY      COULD      FROM       HOW                                          AN         DEC        GET        HOWEVER                                      AND        DECEMBER   GO         I                                            ANOTHER    DID        GOT        I'D                                          ANY        DO         HAD        I'LL                                         ANYBODY    DOE        HAPPEN     I'M                                          ANYMORE    DUE        HARDLY     I'VE                                         ANYONE     DURING     HAS        I.E                                          ANYTHING   E.G        HAVE       IF                                           APR        EACH       HAVING     IN                                           APRIL      EIGHT      HE         INTO                                         ARE        EIGHTEEN   HENCE      IS                                           AROUND     EITHER     HER        IT                                           AS         ELEVEN     HER'N      ITS                                          ASIDE      EVEN       HERE       ITSELF                                       ASK        EVEN-      HEREAFTER  JAN                                                     TUALLY                                                             AT         EVER       HEREBY     JANUARY                                      AUG        EVERYBODY  HEREIN     JUL                                          AUGUST     EVERYMAN   HEREIN-    JULY                                                               AFTER                                                   AWAY       EVERYONE   HEREIN-    JUN                                                                SOFAR                                                   JUNE       ONLY       SOMEBODY   THREE                                        JUST       ONTO       SOMEONE    THROUGH                                      LIKE       OR         SOMETHING  THUR                                         MANY       OTHER      SOMETIME   THURSDAY                                     MAR        OTHERWISE  SOMEWHERE  THUS                                         MARCH      OUGHT      SOONER     THY                                          MAY        OUR        STILL      THYSELF                                      ME         OUR'N      SUCCUSSION TILL                                         MIGHT      OURSELF    SUCH       TO                                           MINE       OURSELVE   SUN        TODAY                                        MON        OUT        SUNDAY     TOMORROW                                     MONDAY     OVER       TAKE       TOO                                          MORE       P.M        TEN        TUB                                          MUCH       PERHAP     THAE       TUESDAY                                      MUST       QUIBUS     THAN       TWELVE                                       MY         QUITE      THAT       TWENTY                                       MYSELF     RATHER     THE        TWO                                          N.S        REALLY     THEE       UN                                           NANE       REV        THEIR      UNDER                                        NEITHER    SAID       THEIRSELF  UNLESS                                       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             O.S        SEVERAL    THERETO    WERE                                         OCT        SHE        THEREWITH  WHAT                                         OCTOBER    SHOULD     THESE      WHATE'ER                                     OF         SINCE      THEY       WHATEVER                                     OFF        SIR        THIRTEEN   WHATSOE'ER                                   OFTEN      SIX        THIS       WHATSOEVER                                   ON         SIXTEEN    THOSE      WHEN                                         ONE        SO         THOU       WHENEVER                                     ONESELF    SOME       THOUGH     WHENSOEVER                                   WHERE      17TH                                                               WHEREBY    18TH                                                               WHEREEVER  19TH                                                               WHEREIN    2D                                                                 WHETHER    2ND                                                                WHICH      20TH                                                               WHICHEVER  21ST                                                               WHICHSOEVER                                                                              3D                                                                 WHILE      3RD                                                                WHO        4TH                                                                WHOEVER    5TH                                                                WHOM       6TH                                                                WHOMSOEVER 7TH                                                                WHOSE      8TH                                                                WHOSESOEVER                                                                              9TH                                                                WHOSO                                                                         WHOSOEVER                                                                     WHY                                                                           WILL                                                                          WITH                                                                          WITHIN                                                                        WITHOUT                                                                       WOULD                                                                         YA                                                                            YE                                                                            YES                                                                           YESTERDAY                                                                     YET                                                                           YON                                                                           YOU                                                                           YOUR                                                                          YOUR'N                                                                        YOURSELF                                                                      1ST                                                                           10TH                                                                          11TH                                                                          12TH                                                                          13TH                                                                          14TH                                                                          15TH                                                                          16TH                                                                          ______________________________________                                    

    ______________________________________                                        List B                                                                        Example of COMPANY INDICATOR LIST                                             Copyright 1995                                                                LEXIS-NEXIS, a Division of Reed Elsevier Inc.                                 ______________________________________                                                   BROS                                                                          BROS.                                                                         BROTHERS                                                                      CHARTERED                                                                     CHTD                                                                          CHTD.                                                                         CL                                                                            CL.                                                                           CO                                                                            CO.                                                                           COMPANY                                                                       CORP                                                                          CORP.                                                                         CORPORATION                                                                   CP                                                                            CP.                                                                           ENTERPRISES                                                                   GP                                                                            GP.                                                                           GROUP                                                                         INC                                                                           INC.                                                                          INCORP                                                                        INCORP.                                                                       INCORPORATED                                                                  INE                                                                           INE.                                                                          LIMITED                                                                       LNC                                                                           LNC.                                                                          LTD                                                                           LTD.                                                               ______________________________________                                    

    ______________________________________                                        List C                                                                        Example of PRODUCT NAME LIST                                                  Copyright 1995                                                                LEXIS-NEXIS, a Division of Reed Elsevier Inc.                                 ______________________________________                                        240sx         Infinity       Reebok                                           300sx         Ingres         Rolaids                                          4-Runner      JVC            SA8                                              7Up           Jaguar         Sable                                            Access        Jeep           Sentra                                           Adobe         Keds           Seven-Up                                         Altima        Kleenex        Solaris                                          Arid          L.O.C.         Sony                                             Avia          Lexus          Sprite                                           B-17          Linux          Suave                                            B17           Lotus          Sun                                              BMW           Magnavox       Sybase                                           Bayer         Maxima         Taurus                                           Blazer        Mercedes       Tide                                             Bounty        Minolta        Toshiba                                          Camary        Mitsubishi     Tums                                             Cannon        Mustang        Tylenol                                          Chevy         Nike           Windex                                           Cirrus        Nikon          Windows                                          Coke          O52            Yashika                                          Converse      Oracle         Zoom                                             Corvette      P100                                                            Etonic        P120                                                            Excel         P133                                                            F-14          P60                                                             F-15          P75                                                             F-16          P90                                                             F-18          Paradox                                                         F-22          Pepsi                                                           F14           Preparation-H                                                   F15           Puffs                                                           F16           Puma                                                            F18           Quicken                                                         F22           Rave                                                            ______________________________________                                    

    ______________________________________                                        List D                                                                        Example of ORGANIZATION INDICATOR LIST                                        Copyright 1995                                                                LEXIS-NEXIS, a Division of Reed Elsevier Inc.                                 ______________________________________                                        ADMINISTRATION      MEN                                                       AGENCY              ORGANIZATION                                              ARMY                PARLIAMENT                                                ASSEMBLY            PARLIMENT                                                 ASSOCIATION         PARTY                                                     BOARD               REPUBLIC                                                  BUREAU              SCHOOL                                                    CENTER              SENATE                                                    CHURCH              SOCIETY                                                   CLINIC              TEAM                                                      CLUB                UNION                                                     COLLEGE             UNIVERSITY                                                COMMISSION                                                                    COMMITTEE                                                                     COMMUNITY                                                                     CONGRESS                                                                      COUNCIL                                                                       COURT                                                                         CULT                                                                          DEPARTMENT                                                                    DEPT                                                                          FACTION                                                                       FEDERATION                                                                    FOUNDATION                                                                    GOVERNMENT                                                                    GUILD                                                                         HOSPITAL                                                                      HOUSE                                                                         INDUSTRY                                                                      INSTITUTE                                                                     LEAGUE                                                                        ______________________________________                                    

    ______________________________________                                        List E                                                                        Example of ENGLISH FIRST-NAME LIST                                            Copyright 1995                                                                LEXIS-NEXIS, a Division of Reed Elsevier Inc.                                 ______________________________________                                        AARON      ADOLF      ALBERT      ALLECIA                                     ABAGAIL    ADOLPH     ALBERTA     ALLEEN                                      ABBIE      ADOLPHUS   ALBIN       ALLEGRA                                     ABBY       ADORA      ALDO        ALLEN                                       ABE        ADRIAN     ALDUS       ALLENE                                      ABEGAIL    ADRIANE    ALEC        ALLICIA                                     ABEL       ADRIANNE   ALECIA      ALLIE                      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                               CLEVON     CORNELIA   DAISEY      DARWIN                                      CLIFF      CORNELIUS  DAISY       DARYL                                       CLIFFORD   CORRIE     DALE        DASHA                                       CLIFT      CORRINE    DALTON      DAVE                                        CLIFTON    CORRINNE   DAMIAN      DAVEY                                       CLINT      CORRY      DAMIEN      DAVID                                       CLINTON    CORTNEY    DAMION      DAVIDA                                      CLIO       CORY       DAMON       DAVIE                                       CLITUS     COSMO      DAN         DAVY                                        CLIVE      COUNTEE    DAN'L       DAWN                                        CLOVIA     COURTLAND  DANA        DEAN                                        CLOVIS     COURTNEY   DANIEL      DEANDRA                                     CLOYD      COY        DANIELLA    DEANE                                       CLYDE      CRAIG      DANIELLE    DEANNA                                      CODELL     CRIS       DANNA       DEANNE                                      COLBERT    CRISPIN    DANNY       DEB                                         COLE       CRISPUS    DANO        DEBBI                                       COLEEN     CRISSIE    DANUTA      DEBBIE                                      COLETTE    CRISSY     DAPHNE      DEBBY                                       COLIN      CRISTABEL  DARBIE      DEBORA                                      COLITA     CRISTA-    DARBY       DEBORAH                                                BELLE                                                              COLLEEN    CRYSTAL    DARCEE      DEBRA                                       COLLETTE   CURLESS    DARCEY      DEBS                                        COLLIN     CURLY      DARCI       DEDIE                                       COLON      CURT       DARCIE      DEE                                         CONNIE     CURTIS     DARCY       DEEANN                                      CONNY      CY         DARIEN      DEEANNE                                     CONRAD     CYBIL      DARIO       DEEDEE                                      CONROY     CYBILL     DARIUS      DEIDRE                                      CONSTANCE  CYNDI      DARLA       DEL                                         CONSTANTIA CYNDY      DARLEEN     DELAINE                                     COOKIE     CYNTHIA    DARLENE     DELANE                                      CORA       CYRIL      DARLINE     DELANO                                      CORABELLE  CYRILL     DARLYNE     DELBERT                                     CORDELIA   CYRILLA    DARNELL     DELIA                                       COREY      CYRUS      DAROLD      DELL                                        CORINE     DABNEY     DARREL      DELLA                                       CORINNE    DACIA      DARRELL     DELLO                                       CORKIE     DACIE      DARREN      DELMA                                       DELMAR     DEVORAH    DONELL      DUAIN                                       DELMAS     DEWANE     DONELLE     DUAINE                                      DELMO      DEWAYNE    DONICE      DUANE                                       DELNO      DEWEY      DONIS       DUB                                         DELORES    DEWITT     DONNA       DUDLEY                                      DELORIS    DEXTER     DONNELL     DUEL                                        DELOY      DEZ        DONNELLE    DUELL                                       DELTA      DIAHANN    DONME       DUFF                                        DEMETRICE  DIANA      DONNY       DUFFY                                       DEMETRIUS  DIANE      DONOVAN     DUGALD                                      DENARD     DIANNA     DORA        DUKE                                        DENE       DIANNE     DORCAS      DULCIE                                      DENICE     DICK       DORCE       DULSA                                       DENILLE    DICKEY     DOREEN      DUNCAN                                      DENIS      DICKIE     DORI        DURWARD                                     DENISE     DIDI       DORIAN      DURWOOD                                     DENNIE     DIEDRE     DORIE       DUSTIN                                      DENNIS     DIERDRE    DORIENNE    DUSTY                                       DENNY      DIETER     DORINE      DWAIN                                       DENNYS     DIETRICH   DORIS       DWAINE                                      DENORRIS   DIMITRI    DOROTHA     DWAYNE                                      DEO        DINA       DOROTHEA    DWIGHT                                      DEON       DINAH      DOROTHY     DYLAN                                       DEREK      DINO       DORRANCE    DYNAH                                       DEREWOOD   DION       DORRIS      EARL                                        DERICK     DIRK       DORSEY      EARLE                                       DERL       DIXIE      DORTHIE     EARLENE                                     DERMOT     DMITRI     DORTHY      EARLINE                                     DERMOTT    DOLLIE     DOSHIE      EARNEST                                     DERRALL    DOLLY      DOT         EARNESTINE                                  DERREK     DOLORES    DOTTIE      EARTHA                                      DERRICK    DOM        DOTTY       EARTHEL                                     DERRY      DOMENIC    DOTY        EBEN                                        DERWOOD    DOMENICK   DOUG        EBENEEZER                                   DESDEMONA  DOMER      DOUGIE      EBENEZER                                    DESI       DOMINIC    DOUGLAS     EBERHARD                                    DESIRE     DOMINICK   DOUGLASS    EBONY                                       DESIREE    DOMINICKA  DOY         ED                                          DESMOND    DOMINIQUE  DOYLE       EDD                                         DESMUND    DON        DREW        EDDIE                                       DEVON      DONALD     DRU         EDDY                                        EDGAR      ELEONORA   ELSIE       EPHRIAM                                     EDGER      ELI        ELTON       ERASMUS                                     EDIE       ELIAS      ELVA        ERBIN                                       EDISON     ELIC       ELVERT      ERIC                                        EDITA      ELIJAH     ELVIE       ERICA                                       EDITH      ELINORE    ELVIN       ERICH                                       EDMOND     ELIOT      ELVIRA      ERICK                                       EDMUND     ELISABETH  ELVIS       ERIK                                        EDNA       ELISE      ELVON       ERIKA                                       EDRIS      ELISHA     ELWOOD      ERIN                                        EDSEL      ELISSA     ELY         ERLAND                                      EDUARD     ELIUS      ELZA        ERLE                                        EDWARD     ELIZA      EMELDA      ERMA                                        EDWIN      ELIZABETH  EMERiC      ERMAN                                       EDWINA     ELIZAR     EMERY       ERNEST                                      EDY        ELKE       EMETT       ERNESTINE                                   EDYTH      ELLA       EMIL        ERNESTO                                     EDYTHE     ELLE       EMILE       ERNIE                                       EFFIE      ELLEN      EMILIE      ERNST                                       EFRAIM     ELLERY     EMILY       ERROL                                       EGBERT     ELLIE      EMMA        ERVAN                                       EGIDIO     ELLIET     EMMALINE    ERVEN                                       EILEEN     ELLIOT     EMMERY      ERVIN                                       ELA        ELLIOTT    EMMET       ERWIN                                       ELAINE     ELLIS      EMMETT      ESAU                                        ELAYNE     ELLY       EMMIE       ESMERELDA                                   ELBA       ELLYN      EMMOT       ESTA                                        ELBERT     ELMA       EMMOTT      ESTEL                                       ELBERTA    ELMER      EMMY        ESTELA                                      ELDA       ELMIRA     EMOGENE     ESTELLA                                     ELDINE     ELMO       EMORY       ESTELLE                                     ELDON      ELNOR      ENDORA      ESTHR                                       ELDORA     ELNORA     ENDRE       ESTHA                                       ELE        ELOI       ENGBLBERT   ESTHER                                      ELEANOR    ELOISE     ENID        ETHAN                                       ELEANORA   ELOUISE    ENISE       ETHEL                                       ELEANORE   ELOY       ENNIS       ETHELENE                                    ELECTRA    ELRIC      ENOCH       ETHELINE                                    ELENA      ELROY      ENOLA       ETHYL                                       ELENORA    ELSA       ENZO        ETIENNE                                     ELENORE    ELSBETH    EPHRAIM     ETTA                                        ETTIE      FARRELL    FRANCIA     GALE                                        EUDORA     FARRIS     FRANCINE    GALLON                                      EUFA       FATIMA     FRANCIS     GARETH                                      EUGENE     FAUN       FRANCOIS    GARLAND                                     EUGENIA    FAWN       FRANCOISE   GARNET                                      EUGENIE    FAY        FRANK       GAROLD                                      EUGENIO    FAYE       FRANKIE     GARRET                                      EULA       FELECIA    FRANKLIN    GARRETT                                     EULALEE    FELICIA    FRANKLYN    GARRIE                                      EULALIE    FELICITY   FRANKY      GARRY                                       EULOGIO    FELIX      FRANNIE     GARTH                                       EUNACE     FELIZ      FRANNY      GARVIN                                      EUNICE     FERD       FRANZ       GARY                                        EUPHEMIA   FERDINAND  FRANZI      GASTON                                      EUSTACE    FERGUS     FRANZIE     GAVIN                                       EVA        FERN       FRANZY      GAY                                         EVALEE     FERREL     FRED        GAYE                                        EVAN       FERRELL    FREDA       GAYLE                                       EVANDER    FERRIS     FREDDIE     GAYLORD                                     EVANGELINE FIDELE     FREDDY      GEARY                                       EVE        FIFI       FREDERICH   GEMMA                                       EVELYN     FILBERT    FREDERICK   GENA                                        EVERETT    FILIPPO    FREDERIK    GENE                                        EVERETTE   FIONA      FREEMAN     GENEVA                                      EVETTE     FITZ       FREIDA      GENEVIEVE                                   EVITA      FLETCHER   FRIEDA      GENIE                                       EWALD      FLO        FRIEDRICH   GENNARO                                     EWIN       FLOR       FRITZ       GENNIE                                      EZEKIEL    FLORA      FRONA       GENNIFER                                    BZRA       FLORANCE   FYODOR      GENNY                                       FABIAN     FLORENCE   GABBEY      GENO                                        FABIEN     FLORIDA    GABBIE      GEO                                         FAIRLEIGH  FLOSSIE    GABE        GEOFF                                       FAITH      FLOSSY     GABRIEL     GEOFFREY                                    FANNIE     FLOYD      GABRIELE    GEORGE                                      FANNY      FONDA      GABRIELLE   GEORGES                                     FARAH      FONTAINE   GABY        GEORGETTE                                   FARLEIGH   FORD       GAEL        GEORGIA                                     FARLEY     FORREST    GAETANO     GEORGIE                                     FARRAH     FRAN       GAGE        GEORGINA                                    FARREL     FRANCES    GAIL        GERALD                                      GERALDINE  GIULIA     GREGGORY    GWENETTA                                    GERARD     GIUSEPPE   GREGORY     GWENETTE                                    GERD       GIUSEPPI   GRETA       GWENITH                                     GERDINE    GIUSEPPINA GRETCHEN    GWENN                                       GERHARD    GLADIS     GRETEL      GWENNE                                      GERI       GLADYCE    GRIFF       GWENNETH                                    GERMAIN    GLADYS     GRIFFIN     GWENYTH                                     GERMAINE   GLEN       GRIFFITH    GWYEN                                       GEROLD     GLENDA     GROVER      GWYLLEN                                     GEROME     GLENDON    GUENTHER    GWYN                                        GERRIE     GLENN      GUERINO     GWYNETH                                     GERRIT     GLENNA     GUIDO       GWYNNE                                      GERRY      GLENME     GUILLERMINA GYULA                                       GERT       GLENNIS    GUILLERMO   HAILEY                                      GERTA      GLENNON    GUISEPPE    HAL                                         GERTIE     GLORIA     GUNNER      HALLIE                                      GERTRUDE   GLYN       GUNTER      HALLY                                       GEZA       GLYNDA     GUNTHER     HAMISH                                      GIA        GLYMS      GUS         HAMPTON                                     GIACOMO    GLYNN      GUSSIE      HANK                                        GIDEON     GLYNMS     GUSSY       HANNA                                       GIFFORD    GODFREY    GUST        HANNAH                                      GIGI       GODFRY     GUSTAF      HANNE                                       GIL        GODWIN     GUSTAV      HANNES                                      GILBERT    GOLDA      GUSTAVE     HANNIBAL                                    GILDA      GOLDIE     GUSTAVUS    HANS                                        GILES      GOLDY      GUSTOV      HANSELL                                     GILLIAN    GOMER      GUY         HARLAN                                      GILLIGAN   GORDAN     GWEN        HARLEN                                      GINA       GORDON     GWENDA      HARLEY                                      GINGER     GOTTFRIED  GWENDALYN   HARLIE                                      GINNI      GRACE      GWENDEN     HARLY                                       GINNIE     GRACIA     GWENDLYN    HARMIE                                      GINNY      GRACIE     GWENDOLA    HARMON                                      GINO       GRADY      GWENDOLEN   HAROL                                       GIORA      GRAEME     GWENDOLINE  HAROLD                                      GIOVANNA   GRAHAM     GWENDOLY    HARREL                                      GIOVANNI   GRANT      GWENDOLYN   HARRIET                                     GISELA     GRAYCE     GWENDOLYNE  HARRIETT                                    GISELLA    GREG       GWENDY      HARRIETTA                                   GISELLE    GREGG      GWENETH     HARRIS                                      HARRISON   HENRYK     HOLLIS      ILA                                         HARROLD    HERE       HOLLISTER   ILAH                                        HARRY      HERBERT    HOLLY       ILEEN                                       HARVEY     HERBIE     HOLLYANN    ILENA                                       HARVIE     HERBY      HOLLYANNE   ILENE                                       HASSLE     HERCULE    HOMER       ILSE                                        HATTIE     HERLINDA   HONEY       IMELDA                                      HATTY      HERM       HONORIA     IMOGENE                                     HAYDEE     HERMA      HOPE        INA                                         HAYDEN     HERMAN     HORACE      INGA                                        HAYDON     HERMANN    HORST       INGE                                        HAYLEY     HERMIE     HORTENSE    INGGA                                       HAYWOOD    HEROLD     HORTENSIA   INGRAM                                      HAZEL      HERSCH     HORTON      INGRID                                      HEATHCLIFF HERSCHEL   HOSBA       IOLA                                        HEATHER    HERSHEL    HOWARD      IONA                                        HECTOR     HESTER     HOWIE       IONE                                        HEDDA      HESTHER    HOYT        IRA                                         HEDDIE     HETTA      HUBERT      IRAD                                        HEDWIG     HETTIE     HUEL        IRENA                                       HEIDI      HETTLE     HUEY        IRENE                                       HEINRICH   HETTY      HUGH        IRIS                                        HEINZ      HEYWOOD    HUGO        IRL                                         HELAINE    HEZEKIAH   HULDA       IRMA                                        HELEN      HILARY     HULDAH      IRVIN                                       HELENA     HILDA      HUMPHREY    IRVING                                      HELENE     HILDE      HUNTINGTON  IRWIN                                       HELGA      HILDEGARD  HURSCHEL    ISAAC                                       HELMUT     HILDE-     HY          ISAAK                                                  GARDE                                                              HELMUTH    HILDRED    HYACINTH    ISABEL                                      HELOISE    HILDUR     HYMAN       ISABELLA                                    HENDRIK    HILLARD    IAIN        ISABELLE                                    HENDRIKA   HILLARY    IAN         ISAC                                        HENNIE     HILLERY    ICHABOD     ISADOR                                      HENNY      HIRAM      IDA         ISADORA                                     HENRI      HIRSCH     IGGY        ISADORE                                     HENRIETA   HOBART     IGNATIUS    ISMAH                                       HENRIETTA  HOBERT     IGNATZ      ISHAM                                       HENRIETTE  HOLLEY     IGNAZ       ISHMAEL                                     HENRIK     HOLLI      IGOR        ISIAH                                       HENRY      HOLLIE     IKE         ISIDOR                                      ISIDORE    JAN        JEMIMA      JILLIAN                                     ISMAEL     JANA       JEMMA       JIM                                         ISOM       JANE       JEMMIE      JIMBO                                       ISRAEL     JANEEN     JEMMY       JIMME                                       ISTVAN     JANEL      JENNIE      JIMMIE                                      ITA        JANELL     JENNIFER    JIMMY                                       IVA        JANELLE    JENNY       JINNY                                       IVAN       JANET      JENS        JO                                          IVANA      JANICE     JERALD      JOAB                                        IVAR       JANIE      JERALDINE   JOACHIM                                     IVARS      JANINE     JERE        JOAN                                        IVETTE     JANIS      JEREMIAH    JOANN                                       IVEY       JANISE     JEREMIAS    JOANNA                                      IVIE       JANNA      JEREMY      JOANNE                                      IVO        JAQUELYN   JERI        JOB                                         IVONNE     JARED      JERIANE     JOCELYN                                     IVOR       JARRETT    JERIE       JOCILYN                                     IVORY      JARRYL     JERMAIN     JOCLYN                                      IVY        JARVIS     JERMAINE    JODI                                        IZAAK      JAS        JEROL       JODIE                                       IZZIE      JASMINE    JEROLD      JODY                                        IZZY       JASON      JEROME      JOE                                         JAC        JASPER     JERRALD     JOEL                                        JACK       JAY        JERRED      JOETTE                                      JACKI      JAYME      JERRELD     JOEY                                        JACKIE     JAYMEE     JERRELL     JOFFRE                                      JACKSON    JAYNE      JERRI       JOH                                         JACKY      JEAN       JERRIE      JOHANN                                      JACOB      JEANETTE   JERROLD     JOHANNES                                    JACOBUS    JEANIE     JERRY       JOHN                                        JACQUELIN  JEANNE     JERZY       JOHNIE                                      JACQUELINE JEANNETTE  JESS        JOHNNA                                      JACQUELYN  JEANNJNE   JESSE       JOHNNIE                                     JACQUES    JEBEDIAH   JESSICA     JOHNNY                                      JADE       JED        JESSIE      JOJO                                        JAKE       JEDEDIAH   JETHRO      JOLENE                                      JAKOB      JEFEREY    JETTA       JON                                         JAMES      JEFF       JETTIE      JONAH                                       JAMEY      JEFFEREY   JEWEL       JONAS                                       JAMIE      JEFFIE     JEWELL      JONATHAN                                    JAMYE      JEFFREY    JILL        JONATHON                                    JONELL     JULIUS     KELSEY      KORNELIA                                    JOM        JUNE       KELVIN      KORNELIUS                                   JONNY      JUNEY      KEN         KRAIG                                       JORDAN     JUNIE      KENDALL     KRIS                                        JORDON     JUNIOR     KENDELL     KRISTA                                      JOSEA      JUNIUS     KENDRICK    KRISTEN                                     JOSELYN    JURGEN     KENNETH     KRISTI                                      JOSEPH     JUSTEN     KENNY       KRISTIE                                     JOSEPHA    JUSTICE    KENT        KRISTIN                                     JOSEPHINE  JUSTIN     KERI        KRISTINE                                    JOSEY      JUSTINE    KERMIT      KRISTOFFER                                  JOSH       KALVIN     KERRIE      KRISTY                                      JOSHUA     KARA       KERRY       KURT                                        JOSIAH     KAREN      KERSTIN     KYLE                                        JOSIE      KARIN      KERVIN      KYM                                         JOY        KARL       KERWIN      LACEY                                       JOYCE      KARLA      KEVIN       LACIE                                       JOYCEANN   KAROLA     KIERSTEN    LACY                                        JOZEF      KASEY      KILIAN      LADON                                       JOZSEF     KASPAR     KILLIAN     LAINE                                       JUBAL      KASPER     KIM         LALAH                                       JUD        KATE       KIMBER      LAMAR                                       JUDAH      KATERINA   KIMBERLEE   LAMARTINE                                   JUDAS      KATEY      KIMBERLEIGH LAMBERT                                     JUDD       KATHERINE  KIMBERLEY   LAMONT                                      JUDE       KATHERYN   KIMBERLY    LANA                                        JUDI       KATHLEEN   KIP         LANCE                                       JUDIE      KATHRYN    KIRBY       LANCELOT                                    JUDITH     KATHY      KIRK        LANE                                        JUDY       KATIE      KIRSTEN     LANSON                                      JULEE      KATY       KIRSTI      LARA                                        JULES      KAY        KIRSTIE     LARENE                                      JULIA      KAYE       KIRSTIN     LARONE                                      JULIAN     KAYLEEN    KIRSTY      LARRIS                                      JULIANE    KEENAN     KIT         LARRY                                       JULIANNE   KEISHA     KITTIE      LARS                                        JULIE      KEITH      KITTY       LASKA                                       JULIEN     KELLEY     KLAUS       LASLO                                       JULIENNE   KELLI      KONRAD      LASZLO                                      JULIET     KELLIE     KONSTAN-    LATIC                                                             TINOS                                                   JULIETTE   KELLY      KOOS        LATIMER                                     LAUANNA    LELAH      LEVON       LISE                                        LAUNCIE    LELAND     LEW         LISSA                                       LAURA      LELIA      LEWELL      LIZ                                         LAUREE     LELIO      LEWIS       LIZA                                        LAUREL     LEMMY      LEXIE       LIZABETH                                    LAUREN     LEMUEL     LEXY        LIZZIE                                      LAURENCE   LEN        LIANE       LIZZY                                       LAURETA    LENA       LIBBIE      LLEWELLYN                                   LAURETTA   LENARD     LIBBY       LLOYD                                       LAURETTE   LENDEN     LIDA        LLOYDA                                      LAURI      LENETTE    LIEF        LOELLA                                      LAURIE     LENISE     LILA        LOGAN                                       LAURIEN    LENNIE     LILAC       LOIS                                        LAVELL     LENNY      LILAH       LOLA                                        LAVERA     LENORA     LILE        LOLETA                                      LAVERN     LENORE     LILIAN      LOLITA                                      LAVERNA    LENWOOD    LILIEN      LOLLIE                                      LAVERNE    LEO        LILITH      LOLLY                                       LAVINA     LEOLA      LILLIA      LON                                         LAVIMA     LEON       LILLIAN     LOM                                         LAVONNE    LEONA      LILLIE      LONNA                                       LAWRENCE   LEONARD    LILLY       LONME                                       LDA        LEONID     LILY        LONNY                                       LEA        LEONIDA    LIN         LONSO                                       LEAH       LEONIDAS   LINCOLN     LONZIE                                      LEANDER    LEONORA    LINDA       LONZO                                       LEANE      LEOPOLD    LINDSAY     LONZY                                       LEANN      LERA       LINDSEY     LORA                                        LEANNE     LEROY      LINK        LORAIN                                      LEATHA     LES        LINNEA      LORAINE                                     LEE        LESLIE     LINNIE      LORANE                                      LEEANN     LESTER     LINNY       LORAY                                       LEEANNE    LETA       LINROY      LORAYNE                                     LEENA      LETHA      LINUS       LOREEN                                      LEESA      LETICIA    LINVAL      LOREN                                       LEFFEL     LETITIA    LINWOOD     LORENA                                      LEFTY      LETTIE     LINZIE      LORENE                                      LEIF       LETTY      LINZY       LORETA                                      LEIGH      LEVERNE    LIONEL      LORETTA                                     LEILA      LEVERT     LISA        LORI                                        LEITH      LEVI       LISABETH    LORIN                                       LORNA      LUIGI      MADELENE    MANSEL                                      LORNE      LUKE       MADELINE    MARABEL                                     LORRAINE   LULA       MADELYN     MARC                                        LORRAYNE   LULAH      MADGE       MARCEL                                      LOTHAR     LULU       MADIE       MARCELIN                                    LOTTIE     LUMMIE     MADONNA     MARCELL                                     LOU        LUNA       MAE         MARCELLA                                    LOUELLA    LURLEEN    MAGDA       MARCELLE                                    LOUIE      LURLENE    MAGDALENA   MARCELLUS                                   LOUIS      LURLINE    MAGDALENE   MARCI                                       LOUISA     LUTHER     MAGDALINE   MARCIA                                      LOUISE     LUZ        MAGGIE      MARCIE                                      LOULA      LUZERNE    MAGGY       MARCUS                                      LOURETTA   LYDIA      MAGNUS      MARCY                                       LOVELL     LYLE       MAHALA      MARDA                                       LOVETTA    LYMAN      MAHALIA     MARGARET                                    LOVETTE    LYN        MAIA        MARGE                                       LOWELL     LYNDA      MAIBLE      MARGEAUX                                    LOY        LYNDON     MAIJA       MARGERY                                     LOYAL      LYNN       MAJOR       MARGI                                       LUANN      LYNNA      MAL         MARGIE                                      LUANNA     LYNNE      MALCOLM     MARGO                                       LUANNE     LYNNETTE   MALINDA     MARGOT                                      LUBY       LYNWOOD    MALISSA     MARGRET                                     LUCAS      LYSANDER   MALKA       MARGY                                       LUCIAN     M'LINDA    MALLORIE    MARIAM                                      LUCIE      MABEL      MALLORY     MARIAN                                      LUCIEN     MABELLE    MALORIE     MARIANN                                     LUCILE     MABLE      MALORY      MARIANNE                                    LUCILLA    MAC        MALYNDA     MARIBEL                                     LUCILLE    MACE       MAME        MARIBELLE                                   LUCINDA    MACIE      MAMIE       MARIBETH                                    LUCIUS     MACK       MANDEE      MARIE                                       LUCRETIA   MADALAINE  MANDI       MARIEL                                      LUCY       MADALEINE  MANDIE      MARIETTA                                    LUDLOW     MADALINE   MANDY       MARIETTE                                    LUDWIG     MADALYN    MANFRED     MARILEE                                     LUDWIK     MADDIE     MANICE      MARILYN                                     LUDY       MADDY      MANLEY      MARILYNN                                    LUELLA     MADELAINE  MANLY       MARINA                                      LUGENE     MADELEINE  MANNY       MARIO                                       MARION     MARYBETH   MEAGAN      MEYER                                       MARISSA    MARY-      MEG         MIA                                                    FRANCES                                                            MARJUS     MARYLOU    MEGAN       MIATTA                                      MARJORIE   MASON      MEL         MICAH                                       MARJORY    MATE       MELANIE     MICHAEL                                     MARK       MATEY      MELANY      MICHEL                                      MARKEE     MATHEW     MELBA       MICHELE                                     MARKIE     MATHIAS    MELICENT    MICHELLE                                    MARKOS     MATHILDA   MELINDA     MICK                                        MARKUS     MATILDA    MELISSA     MICKEY                                      MARLA      MATT       MELLICENT   MICKIE                                      MARLENA    MATTHEW    MELODI      MICKY                                       MARLENE    MATTHIAS   MELODIE     MIDGE                                       MARLEY     MATTIE     MELODY      MIKAEL                                      MARLIN     MATTY      MELONIE     MIKAL                                       MARLON     MATTYE     MELONY      MIKE                                        MARMADUKE  MAUD       MELVA       MIKEAL                                      MARNEY     MAUDE      MELVIN      MILAN                                       MARNIE     MAURA      MELVYN      MILDRED                                     MARNY      MAUREEN    MELYNDA     MILES                                       MARSDEN    MAURENE    MENDEL      MILICENT                                    MARSHA     MAUREY     MERCEDEL    MILLARD                                     MARSHAL    MAURICE    MERCEDES    MILLICENT                                   MARSHALL   MAURIE     MERCY       MILLIE                                      MARTA      MAURINE    MEREDETH    MILLY                                       MARTEA     MAURY      MEREDITH    MILO                                        MARTHA     MAVIS      MERIDETH    MILT                                        MARTI      MAX        MERIDITH    MILTON                                      MARTICA    MAXCIE     MERISSA     MIMI                                        MARTIE     MAXCINE    MERLE       MINDY                                       MARTIKA    MAXIE      MERLIN      MINERVA                                     MARTILDA   MAXIM      MERLYN      MINNIE                                      MARTIN     MAXI-      MERREL      MIRANDA                                                MILIAN                                                             MARTY      MAXI-      MERRELL     MIRIAM                                                 MILLIAN                                                            MARV       MAXINE     MERRILL     MISSIE                                      MARVA      MAXWELL    MERRY       MISSY                                       MARVIN     MAY        MERVIN      MISTY                                       MARY       MAYBELLE   MERWIN      MITCH                                       MARYAM     MAYDA      MERWYN      MITCHEL                                     MARYANN    MAYME      MERYL       MITCHELL                                    MARYANNE   MAYNARD    META        MITZI                                       MOE        NAMIE      NEWEL       NORA                                        MOLLIE     NAN        NEWELL      NORAH                                       MOLLY      NANCI      NEWT        NORBERT                                     MONA       NANCIE     NEWTON      NOREEN                                      MONICA     NANCY      NICHAEL     NORM                                        MONIQUE    NANETTE    NICHOLM     NORMA                                       MONTE      NAM        NICHOLAS    NORMAL                                      MONTGOMERY NANNETTE   NICK        NORMAN                                      MONTY      NANNI      NICKI       NORRIS                                      MONY       NANNIE     NICKIE      NORTON                                      MORDECAI   NANNY      NICKODEMUS  NORVAL                                      MOREY      NAOMA      NICKY       NUNZIO                                      MORGAN     NAOMI      NICODEMO    NYLE                                        MORGANA    NAPOLEON   NICODEMUS   OBADIAH                                     MORRIS     NAT        NICOL       OBED                                        MORT       NATALIE    NICOLA      OBEDIAH                                     MORTIMER   NATASHA    NICOLM      OCIE                                        MORTON     NATASSIA   NICOLAS     OCTAVE                                      MORTY      NATHAN     NICOLE      OCTAVIA                                     MOSE       NATHAMEL   NICOLETTE   ODEL                                        MOSES      NATWICK    NICOLLE     ODELL                                       MOZELLE    NAZARETH   NICOLO      ODESSA                                      MULLIGAN   NEAL       NIGEL       ODIE                                        MURIEL     NEALY      NIKA        ODIS                                        MURPHY     NED        NIKE        OGDEN                                       MURRAY     NEDINE     NIKI        OKTAVIA                                     MURRELL    NEIL       NIKITA      OLA                                         MURRY      NEILL      NIKITAS     OLAF                                        MYLES      NELDA      NIKKI       OLAN                                        MYNA       NELL       NILE        OLEG                                        MYRA       NELLE      NILES       OLEN                                        MYRAH      NELLIE     NILS        OLGA                                        MYRAL      NELLY      NIMROD      OLIN                                        MYREN      NELS       NINA        OLIVE                                       MYRNA      NELSON     NOAH        OLIVER                                      MYRON      NENA       NOE         OLIVIA                                      MYRTLE     NERO       NOEL        OLLEN                                       NADENE     NERSES     NOLA        OLLIE                                       NADIA      NESTOR     NOLAN       OLOF                                        NADINE     NETFIE     NONA        OMER                                        NADJA      NEVILLE    NONNIE      ONAL                                        ONEL       PAGE       PENNY       PRUDENCE                                    ONNIK      PAIGE      PER         PRUE                                        OPAL       PAM        PERCIVAL    PRUNELLA                                    OPEL       PAMELA     PERCY       QUEEN                                       OPHELIA    PANSY      PERRY       QUEENIE                                     OPRAH      PAOLO      PERSIS      QUEENY                                      ORA        PARL       PETE        QUENTIN                                     ORAL       PARNELL    PETER       QUINCY                                      ORAN       PARRY      PETRA       QUINNIE                                     OREN       PASCAL     PETRO       RACHEL                                      ORESTE     PASCHAL    PETROS      RACHELLE                                    ORIN       PASQUALE   PHEBE       RAE                                         ORLAN      PAT        PHEUM       RAIFORD                                     ORLEN      PATIENCE   PHIDIAS     RALPH                                       ORLIN      PATRICE    PHIL        RAMONA                                      ORLYN      PATRICIA   PHILBERT    RANDAL                                      ORPHA      PATRICK    PHILIP      RANDALL                                     ORSON      PATSY      PHILIPPE    RANDI                                       ORTON      PATTI      PHILLIP     RANDIE                                      ORTRUD     PATTIE     PHILO       RANDOLF                                     ORVAL      PATTY      PHINEAS     RANDOLPH                                    ORVID      PAUL       PHOEBE      RANDY                                       ORVIL      PAULA      PHYLLIS     RANSOM                                      ORVILLE    PAULETTE   PIA         RAPHAEL                                     OSBERT     PAULINA    PIER        RAQUEL                                      OSCAR      PAULINE    PIERCE      RAY                                         OSGOOD     PAVEL      PIERINA     RAYFORD                                     OSIE       PEARCE     PIERRE      RAYMOND                                     OSSIE      PEARL      PIERS       RBT                                         OSWALD     PEARLENE   PIETER      REAGAN                                      OTHA       PEARLIE    PIETRO      REBA                                        OTHIS      PEARLINE   PLATO       REBECCA                                     OTIS       PEARLY     POINDEXTER  RECTOR                                      OTTIS      PEDER      POLUE       REED                                        OTTO       PEG        POLLY       REGAN                                       OVE        PEGGI      PORTIA      REGGIE                                      OVETA      PEGGIE     PRECY       REGGY                                       OWEN       PEGGY      PRESTON     REGINA                                      OZZIE      PENELOPE   PRINCE      REGINALD                                    PADDIE     PENNI      PRINCESS    REGIS                                       PADDY      PENNIE     PRISCILLA   REID                                        REINHARDT  RIPLEY     RONNETTE    ROXIE                                       REINHOLD   RISA       RONNIE      ROXY                                        REMI       RITA       RONNY       ROY                                         REMO       RITCHIE    ROOSEVELT   ROYAL                                       REMUS      RITHA      RORY        ROYCE                                       RENA       ROB        ROSALEE     ROZALIA                                     RENATA     ROBBI      ROSALIA     ROZALIE                                     RENATE     ROBBIE     ROSALIE     RUBAN                                       RENE       ROBBIN     ROSALIND    RUBEN                                       RENEE      ROBBY      ROSALINDA   RUBENA                                      RETA       ROBERT     ROSALYN     RUBERT                                      RETHA      ROBERTA    ROSALYND    RUBEY                                       REUBAN     ROBIN      ROSAMOND    RUBIE                                       REUBEN     ROBINA     ROSAMONDE   RUBIN                                       REUBENA    ROBINETTE  ROSAMUND    RUBINA                                      REUBIN     ROBYN      ROSAMUNDE   RUBY                                        REUBINA    ROCCO      ROSANNA     RUBYE                                       REVA       ROCHELLE   ROSANNE     RUDDY                                       REX        ROCKY      ROSCOE      RUDOLF                                      REXFORD    ROD        ROSE        RUDOLPH                                     REY        RODDIE     ROSEANN     RUDY                                        REYNALD    RODERIC    ROSEANNE    RUE                                         REYNOLD    RODERICH   ROSEBUD     RUEBEN                                      RHEA       RODERICK   ROSELIN     RUFUS                                       RHINELANDER                                                                              RODGER     ROSELYN     RULOEF                                      RHODA      RODNEY     ROSEMARIE   RUPERT                                      RHONA      RODRICK    ROSEMARY    RUSS                                        RHONDA     ROGER      ROSEMUND    RUSSELL                                     RHYS       ROLAND     ROSEMUNDE   RUSTY                                       RICARD     ROLF       ROSENA      RUTH                                        RICH       ROLFE      ROSETTA     RUTHANNA                                    RICHARD    ROLLO      ROSIE       RUTHANNE                                    RICHELLE   ROMAN      ROSINA      RUTHIE                                      RICHIE     ROMEO      ROSLYN      RUTHLYN                                     RICK       ROMULUS    ROSS        RYAN                                        RICKEY     RON        ROSWELL     SABA                                        RICKI      RONA       ROSWITHA    SABINA                                      RICKIE     RONALD     ROULETTE    SABINE                                      RICKY      RONDA      ROWENA      SABRINA                                     RIKKI      RONDELL    ROWLAND     SADIE                                       RILEY      RONETTE    ROXANNE     SAL                                         SALLI      SHARI      SIDNEY      STACIE                                      SALLIE     SHARLENE   SIEGFRIED   STACY                                       SALLY      SHARON     SIG         STAN                                        SALLYE     SHARYN     SIGFRIED    STANISLAW                                   SALOMON    SHAUN      SIGMUND     STANLEY                                     SAM        SHAWN      SIGNE       STANLY                                      SAMANTHA   SHAWNA     SIGURD      STEFAN                                      SAMMIE     SHAYNE     SILAS       STEFFI                                      SAMMY      SHEARL     SILVIA      STELLA                                      SAMUAL     SHEBA      SILVIO      STEPHAN                                     SAMUEL     SHEENA     SIMEON      STEPHANIE                                   SANDI      SHEILA     SIMON       STEPHEN                                     SANDIE     SHELBY     SIMONE      STERLING                                    SANDRA     SHELDEN    SIMONNE     STEVE                                       SANDY      SHELDON    SISSIE      STEVEN                                      SANJA      SHELIA     SISSY       STEVIE                                      SARA       SHELLEY    SKEET       STEWART                                     SARAH      SHELLY     SKIPPIE     STU                                         SAUL       SHELTON    SKIPPY      STUART                                      SCHUYLER   SHERI      SKYLER      SUANN                                       SCOT       SHERIDAN   SLIM        SUANNE                                      SCOTT      SHERIE     SMEDLEY     SUE                                         SCOTTIE    SHERILYN   SOFIE       SUELLEN                                     SCOTTY     SHERL      SOL         SUMNER                                      SEAMUS     SHERLE     SOLOMAN     SUNNIE                                      SEAN       SHERLEE    SOLOMON     SUNNY                                       SEBASTIAN  SHERMAN    SONDRA      SUSAN                                       SEFERINO   SHERON     SONIA       SUSANA                                      SELDEN     SHERREE    SONJA       SUSANNA                                     SELENA     SHERRI     SONNY       SUSANNAH                                    SELENE     SHERRIE    SONYA       SUSANNE                                     SELINA     SHERRY     SPARKY      SUSETTE                                     SELMA      SHERWIN    SPENCE      SUZAN                                       SELMER     SHERYL     SPENCER     SUZANNE                                     SERENA     SHIRL      SPENSER     SUZELLEN                                    SERINA     SHIRLE     SPIRO       SUZETTE                                     SETH       SHIRLEE    SPIROS      SUZI                                        SEYMORE    SHIRLEY    SPYROS      SUZIE                                       SEYMOUR    SI         STACEY      SUZY                                        SHANE      SIBYL      STACI       SVEN                                        SHANNON    SID        STACIA      SWEN                                        SY         TERRI      THURSTAN    TREVOR                                      SYBIL      TERRILL    THURSTON    TREY                                        SYD        TERRY      TIBOR       TRICIA                                      SYDNEY     TERRYL     TIFFANY     TRILBY                                      SYLVAN     TESS       TILLIE      TRINA                                       SYLVANUS   TESSIE     TILLY       TRISH                                       SYLVENE    TESSY      TIM         TRISTAM                                     SYLVESTER  TEX        TIMMY       TRISTAN                                     SYLVIA     THAD       TIMO        TRIXIE                                      SYLVIE     THADDEUS   TIMOTHY     TRIXY                                       SYLVINA    THADEUS    TINA        TROY                                        TABATHA    THARON     TINO        TRUDIE                                      TABITHA    THEA       TIPHANIE    TRUDY                                       TAD        THEDA      TIPHANY     TWALA                                       TAFFY      THEIMA     TITO        TWILA                                       TALLULAH   THELMA     TITUS       TWYLA                                       TAMARA     THEO       TOBIAS      TYCHO                                       TAMI       THEOBALD   TOBY        TYCHUS                                      TAMMI      THEODIS    TODD        TYCUS                                       TAMMIE     THEODOR    TOLLIE      TYRONE                                      TAMMY      THEODORA   TOLLIVER    UDIE                                        TANCRED    THEODORE   TOLLY       UDY                                         TAMA       THEODORIS  TOM         ULRICH                                      TANJA      THEODOSIA  TOMMIE      ULYSSES                                     TANYA      THEONE     TOMMY       UNA                                         TASHA      THEORA     TONEY       URA                                         TATE       THEOTIS    TOM         URBAIN                                      TATIANA    THERESA    TOMA        URIAS                                       TAUBA      THERESIA   TONY        URSULA                                      TED        THERESSA   TONYA       VACHEL                                      TEDD       THOM       TOOTIE      VADA                                        TEDDY      THOMAS     TOVE        VAL                                         TEE        THOMASINA  TRACEE      VALDA                                       TELMA      THOR       TRACEY      VALENTIN                                    TENA       THORA      TRACI       VALENTINE                                   TENCH      THORE      TRACIE      VALENTINO                                   TERENCE    THORNTON   TRACY       VALERIA                                     TERESA     THORVALD   TRAVER      VALERIE                                     TERRANCE   THOS       TRAVIS      VALTER                                      TERRELL    THURMAN    TRENA       VANCE                                       TERRENCE   THURMOND   TRENT       VANDER                                      VANDY      VINCENT    WAYMAN      WINNIFRED                                   VANBSSA    VINNIE     WAYNE       WINNY                                       VAUGHAN    VINNY      WELDEN      WINONA                                      VAUGHN     VIOLA      WELDON      WINSLOW                                     VEDA       VIOLET     WELLS       WINSTON                                     VELLA      VIRACE     WENDEL      WINTHROP                                    VELMA      VIRDA      WENDELL     WINTON                                      VELVA      VIRGIE     WENDI       WM                                          VEOLA      VIRGIL     WENDY       WOLFGANG                                    VERA       VIRGINIA   WERNER      WOODIE                                      VERDEEN    VIRGIMUS   WES         WOODROW                                     VERDELL    VITA       WESLEY      WOODRUFF                                    VERGA      VITINA     WESLIE      WOODY                                       VERGIL     VITINO     WESTLEY     WYATT                                       VERLIE     VITO       WHITNEY     WYLA                                        VERLIN     VITTORIA   WIDDIE      XAVIER                                      VERLON     VIVIAN     WILBER      XAVIERA                                     VERLYN     VIVIANNE   WILBERFORCE YARDLEY                                     VERN       VIVIEN     WILBERT     YETTA                                       VERNA      VLAD       WILBUR      YOLANDA                                     VERNARD    VLADIMIR   WILDA       YOSEF                                       VERNE      VOL        WILEY       YVES                                        VERNEST    VON        WILFORD     YVETTB                                      VERNESTINE VONDA      WILFRED     YVONNE                                      VERNICE    VONNA      WILHELM     ZACHARIA                                    VERNIE     WADE       WILHELMENA  ZACHARIAH                                   VERNON     WALDEMAR   WILHELMINA  ZACHARY                                     VERONICA   WALDO      WILHEMENA   ZACK                                        VERSA      WALDORF    WILHEMINA   ZALPH                                       VERSIE     WALLACE    WILLARD     ZANE                                        VI         WALLIE     WILLIAM     ZEB                                         VIC        WALLY      WILLIE      ZEBADIAH                                    VICKI      WALT       WILLIS      ZEBEDEE                                     VICKIE     WALTER     WILLMA      ZECHARIAH                                   VICKY      WANDA      WILLY       ZEF                                         VICTOR     WARD       WILMA       ZEFF                                        VICTORIA   WARREN     WILMAR      ZEKE                                        VIDAL      WASHING-   WILMOT      ZELDA                                                  TON                                                                VIE        WAYLAN     WINFRED     ZELIA                                       VILMA      WAYLEN     WINIFRED    ZELIG                                       VINCE      WAYLON     WINNIE      ZELL                                        ZELLA                                                                         ZELLE                                                                         ZELMA                                                                         ZENA                                                                          ZENITH                                                                        ZENO                                                                          ZENOBIA                                                                       ZENON                                                                         ZEPHERY                                                                       ZETA                                                                          ZETTA                                                                         ZEV                                                                           ZILLA                                                                         ZILLAH                                                                        ZINA                                                                          ZITA                                                                          ZIVKO                                                                         ZOE                                                                           ZOLLIE                                                                        ZOLLY                                                                         ZORA                                                                          ZULA                                                                          ZYGMUND                                                                       ZYGMUNT                                                                       ______________________________________                                    

Modifications and variations of the above-described embodiments of thepresent invention are possible, as appreciated by those skilled in theart in light of the above teachings. As mentioned, any of a variety ofhardware systems, memory organizations, software platforms, andprogramming languages may embody the present invention without departingfrom its spirit and scope. Moreover, countless variations of thePartition List, company indicators, product names, organizationindicators, English first name list, and resulting Phrase Lists, and thelike, may be employed or produced while remaining within the scope ofthe invention. It is therefore to be understood that, within the scopeof the appended claims and their equivalents, the invention may bepracticed otherwise than as specifically described.

What is claimed is:
 1. A computer-implemented method of processing astream of document text to form a list of phrases that are indicative ofconceptual content of the document, the phrases being used as indexterms and search query terms in full text document searching performedafter the phrase list is formed, the method comprising:partitioning thedocument text into plural chunks of document text, each chunk beingseparated by at least one partition entity from a partition list; andselecting certain chunks as the phrases of the phrase list, based onfrequencies of occurrence of the chunks within the stream of documenttext.
 2. The method of claim 1, wherein the partitioning stepincludes:scanning a portion of the document text stream; comparing thescanned portion of the document text stream to partition entities in thepartition list; substituting a partition tag for portions of thedocument text stream which match a partition entity; generating a textchunk list; scanning the text chunk list to determine a frequency ofeach text chunk in the text chunk list; and revising the text chunk listto include the respective frequencies of occurrence in association withthe text chunks.
 3. The method of claim 1, wherein the selecting stepincludes:selecting the certain chunks as the phrases of the phrase listbased only on the frequencies of occurrence of the chunks within thestream of document text and on a quantity of words within the chunks. 4.The method of claim 1, wherein:a) the partitioning step includes:a1)scanning a portion of the document text stream; a2) comparing thescanned portion of the document text stream to partition entities in thepartition list; a3) substituting a partition tag for portions of thedocument text stream which match a partition entity; a4) generating atext chunk list; a5) scanning the text chunk list to determine afrequency of each text chunk in the text chunk list; and a6) revisingthe text chunk list to include the respective frequencies of occurrencein association with the text chunks; and b) the selecting step includesselecting the certain chunks as the phrases of the phrase list basedonly on the frequencies of occurrence of the chunks within the stream ofdocument text and on a quantity of words within the chunks.
 5. Themethod of claim 1, wherein the selecting step includes:excluding a chunkfrom being determined as a phrase if the chunk is a single wordbeginning with a lower case letter.
 6. The method of claim 1, whereinthe selecting step includes:determining a chunk as being a phrase if thechunk includes a plurality of words each constituting lower case lettersonly if the chunk occurs at least twice in the document text stream. 7.The method of claim 1, wherein the selecting step includes:determining achunk as being a proper name if the chunk includes a plurality of wordseach having at least a first letter which is upper case.
 8. The methodof claim 1, wherein the selecting step includes:mapping a sub-phrase toa phrase.
 9. The method of claim 1, wherein the selecting stepincludes:mapping single upper case words to their respective propernames.
 10. The method of claim 1, wherein the selecting stepincludes:detecting presence of acronyms; incrementing a count of aproper name corresponding to the respective detected acronyms; andcopying the proper name and the acronym to an acronym list.
 11. Themethod of claim 1, wherein the selecting step includes:combining aphrase list of lower case words with a phrase list of proper names. 12.The method of claim 1, further comprising:reducing the phrase list byconsolidating phrases in the phrase list by using a synonym thesaurus.13. The method of claim 1, further comprising:adding phrases to thephrase list by combining phrases which are separated in the documenttext stream only by prepositions.
 14. The method of claim 1, furthercomprising:trimming the phrase list by eliminating phrases which occurin fewer than a threshold number of document text streams.
 15. Themethod of claim 1, further comprising:categorizing proper names in theproper name list into groups based on corresponding group lists.
 16. Anapparatus of processing a stream of document text to form a list ofphrases that are indicative of conceptual content of the document, thephrases being used as index terms and search query terms in full textdocument searching performed after the phrase list is formed, theapparatus comprising:means for partitioning the document text intoplural chunks of document text, each chunk being separated by at leastone partition entity from a partition list; and means for selectingcertain chunks as the phrases of the phrase list, based on frequenciesof occurrence of the chunks within the stream of document text.
 17. Theapparatus of claim 16, wherein the partitioning means includes:means forscanning a portion of the document text stream; means for comparing thescanned portion of the document text stream to partition entities in thepartition list; means for substituting a partition tag for portions ofthe document text stream which match a partition entity; means forgenerating a text chunk list; means for scanning the text chunk list todetermine a frequency of each text chunk in the text chunk list; andmeans for revising the text chunk list to include the respectivefrequencies of occurrence in association with the text chunks.
 18. Theapparatus of claim 16, wherein the selecting means includes:means forselecting the certain chunks as the phrases of the phrase list basedonly on the frequencies of occurrence of the chunks within the stream ofdocument text and on a quantity of words within the chunks.
 19. Theapparatus of claim 16, wherein:a) the partitioning means includes:a1)means for scanning a portion of the document text stream; a2) means forcomparing the scanned portion of the document text stream to partitionentities in the partition list; a3) means for substituting a partitiontag for portions of the document text stream which match a partitionentity; a4) means for generating a text chunk list; a5) means forscanning the text chunk list to determine a frequency of each text chunkin the text chunk list; and a6) means for revising the text chunk listto include the respective frequencies of occurrence in association withthe text chunks; and b) the selecting means includes means for selectingthe certain chunks as the phrases of the phrase list based only on thefrequencies of occurrence of the chunks within the stream of documenttext and on a quantity of words within the chunks.
 20. Acomputer-readable memory which, when used in conjunction with acomputer, can carry out a phrase recognition method to form a phraselist containing phrases that are indicative of conceptual content of adocument, the phrases being used as index terms and search query termsin full-text document searching performed after the phrase list isformed, the computer-readable memory comprising:computer-readable codefor partitioning document text into plural chunks of document text, eachchunk being separated by at least one partition entity from a partitionlist; and computer-readable code for selecting certain chunks as thephrases of the phrase list based on frequencies of occurrence of thechunks within the stream of document text.
 21. The computer-readablememory of claim 20, wherein the computer-readable code for partitioningincludes:computer-readable code for scanning a portion of the documenttext stream; computer-readable code for comparing the scanned portion ofthe document text stream to partition entities in the partition list;computer-readable code for substituting a partition tag for portions ofthe document text stream which match a partition entity;computer-readable code for generating a text chunk list;computer-readable code for scanning the text chunk list to determine afrequency of each text chunk in the text chunk list; andcomputer-readable code for revising the text chunk list to include therespective frequencies of occurrence in association with the textchunks.
 22. The computer-readable memory of claim 20, wherein thecomputer-readable code for selecting includes:computer-readable code forselecting the certain chunks as the phrases of the phrase list basedonly on the frequencies of occurrence of the chunks within the stream ofdocument text and on a quantity of words within the chunks.
 23. Thecomputer-readable memory of claim 20, wherein:a) the computer-readablecode for partitioning includes:a1) computer-readable code for scanning aportion of the document text stream; a2) computer-readable code forcomparing the scanned portion of the document text stream to partitionentities in the partition list; a3) computer-readable code forsubstituting a partition tag for portions of the document text streamwhich match a partition entity; a4) computer-readable code forgenerating a text chunk list; a5) computer-readable code for scanningthe text chunk list to determine a frequency of each text chunk in thetext chunk list; and a6) computer-readable code for revising the textchunk list to include the respective frequencies of occurrence inassociation with the text chunks; and b) the computer-readable code forselecting includes computer-readable code for selecting the certainchunks as the phrases of the phrase list based only on the frequenciesof occurrence of the chunks within the stream of document text and on aquantity of words within the chunks.
 24. A computer-implemented methodof full-text, on-line searching, the method comprising:a) receiving andexecuting a search query to display at least one current document; b)receiving a command to search for documents having similar conceptualcontent to the current document; c) executing a phrase recognitionprocess to extract phrases allowing full text searches for documentshaving similar conceptual content to the current document, the phraserecognition process including the steps of:c1) partitioning the documenttext into plural chunks of document text, each chunk being separated byat least one partition entity from a partition list; and c2) selectingcertain chunks as the phrases, based on frequencies of occurrence of thechunks within the stream of document text; and d) automatically forminga second search query based at least on the phrases determined in thephrase recognition process so as to allow automated searching fordocuments having similar conceptual content to the current document. 25.The method of claim 24, further comprising:validating phrases recognizedby the phrase recognition process against phrases in a phrase dictionarybefore automatically forming the second search query.
 26. The method ofclaim 24, further comprising:displaying an error message if less than athreshold number of phrases are recognized for the current document. 27.A computer-implemented method of forming a phrase list containingphrases that are indicative of conceptual content of each of a pluralityof documents, which phrases are used as index terms or in documentsearch queries formed after the phrase list is formed, the methodcomprising:a) selecting document text from the plurality of documents;b) executing a phrase recognition process including the steps of:b1)partitioning the document text into plural chunks of document text, eachchunk being separated by at least one partition entity from a partitionlist; and b2) selecting certain chunks as the phrases, based onfrequencies of occurrence of the chunks within the stream of documenttext; and c) forming the phrase list, wherein the phrase listincludes:1) phrases extracted by the phrase recognition process; and 2)respective frequencies of occurrence of the extracted phrases.
 28. Themethod of 27, further comprising:forming a modified phrase list havingonly those phrases whose respective frequencies of occurrence aregreater than a threshold number of occurrences.
 29. The method of 27,further comprising:forming a phrase dictionary based on the phrase listformed in the forming step.
 30. A computer-implemented method of formingphrase lists containing phrases that are indicative of conceptualcontent of documents, which phrases are used as index terms or indocument search queries formed after the phrase list is formed, themethod comprisinga) selecting document text from a sampling of documentsfrom among a larger collection of documents; and b) executing a phraserecognition process to extract phrases to form a phrase list for eachdocument processed, the phrase recognition process including:b1)partitioning the document text into plural chunks of document text, eachchunk being separated by at least one partition entity from a partitionlist; and b2) selecting certain chunks as the phrases of the phrase listbased on frequencies of occurrence of the chunks within the stream ofdocument text.